I recently attend a UX design event at UCLA, which included a presentation by Joselle Ho, Creative Director and Co-Founder of Miso Media, a music education company that got its start in mobile in 2008. It was a fun event as we got to hear Joselle's thoughts on design, and then a panel of judges critiqued some screenshots of real designs others were working on. Below are some of my notes and takeaways
UI != UX
Focus on user's wants, assumptions, emotions.
Design for main use case, bury functionality.
Every excess click reduces engagement 90%.
Replacing "Sign up" with "Learn more" increases sign-ups 350%.
What you say matters.
When you say it matters.
Animations make a big difference (continuity and breaks).
Outside pages need to communicate 2 things: why and how.
Login/Sign up/Help in upper right corner
Call to action must pop out when you squint.
Photo-based navigation preferred over text in studies
Discovery of activity in app: make it a widget, reduce the content amount, make it like a ticker
"ppl who added XYZ also added ABC"notification indicator in menu bar
Blueprint for iPad
Keynote (has hotspot/click targets)
Explainer video as sole thing in homepage: not great
I'm excited about having graduated on Friday from UCLA Anderson. Our commencement speech was entertaining and inspirational, and I wanted to share the top 10 list that Guy Kawasaki shared with us.
Guy shares alma maters with me (Stanford followed by UCLA Anderson), so I identified with him immediately. He was the chief evangelist at Apple and author of ten books (two of which I've read and really enjoyed: Enchantment and The Art of the Start).
You can watch his speech in the video above, and my notes are below.
Guy's Top 10 Practical Tips for Succeeding
1. Aspire to jump to the next curve.
Instead of working on a better sameness
2. Don't worry, be crappy.
When you're on the next curve, it's time to ship.
Ship and then test.
3. Never ask people to do something you wouldn't do.
Whether it's asking customers or employees
Importance of ethics
4. Obey the absolutes.
There's a hard line between right and wrong.
5. Default to yes.
Always thinking "how can I help others?"
6. Drop everything when your boss asks you to do something.
Make your boss or wife look good.
7. Become a baker not an eater.
For baker's, life's not a zero sum game.
8. Hire people better than you.
A people hire A people. B people hire C people (and so on).
Hire people better than you to prevent a "bozo explosion."
(See my notes on the book Who.)
9. Change your mind.
It's a sign of intelligence.
Steve Jobs changed his mind all the time (and convinced you he was right before and after).
10. 10/20/30 rule of PowerPoint
Optimal slides: 10
Optimal time: 20 minutes
Optimal font size: 30
Figure out oldest person in room and divide age by 2 and that is the font size.
11. Suck it up
Need to pay dues.
Do the dirty job.
12. Have children.
True success is happiness in life.
Children are his greatest joy in life.
Nothing came close in his life in joy.
As part of a class I took on biotech, we were assigned to read Science Business by Gary Pisano. I learned a lot from the class and this book, and it really answered a question I've had for a long time: Why does science/medicine move so much more slowly than technology in general?
I learned that the biotech industry as a whole has been barely profitable since its inception, and that there is a severe productivity crisis (productivity as defined by cost per successful drug has been dropping over time, which is very different from something like computer processors which have been dropping in price over time). There is a big "valley of death" between discovery of a compound or process and commercialization. It takes 10 years and $1 billion to get a drug to market, and 1 in 5,000 drugs makes it. WHOA.
People are always optimistic about biotech revolutionizing health, and it hasn't lived up to this potential yet. The book explains many reasons for this and suggests some different approaches and solutions, none of which seems easy or straightforward.
My full notes are below. I'm curious to see how the industry evolves in the future, as many lives could be saved and improved if things change drastically.
I. Preface: The rise of a new industry and a big question
a. Big hopes but disappointing financial returns over time
b. Biotech firms not more productive in R&D than big pharma
c. Fundamental business problems created by science
d. Functional requirements of sector; performance comes from how well it’s managing these (poorly)
i. Risk management
e. Monetizing IP leads to bad info flow, fragmentation, proliferation of new firms
f. Biotech can’t just adopt same methods as high-tech
g. Can sci be a biz?
h. Some businesses doing basic sci; some universities treating sci like biz (selling IP, starting co’s)
i. 30 year history of biotech sector data analyzed
II. Ch. 1: the science-based business: a novel experiment
a. Biotech is convergence of 2 separate realms
b. Science biz one that tries to advance sci, not just use it
c. Sci biz needs unique mgmt
d. Sector profits near zero historically
e. Different norms, values, metrics between sci and biz
f. 3 main factors
i. Profound and persistent uncertainty => needs risk rewarding and mgmt
1. Long time horizons for risk to be resolved
2. Appropriability: ability of biz to capture value from an asset
3. Openness vs. secrecy
ii. Complex and heterogenous nature of scientific knowledge => needs integration
1. Cross disciplinary
iii. Rapid progress => cumulative learning
Part 1: The Science of the business
I. Ch. 2: mapping the scientific landscape
a. Locks and keys
b. Random screening
e. combinatorial chem
h. RNA interference
k. Growing size, complexity, heterogeneity
II. Ch. 3: the complex anatomy of drug R&D
a. Can save or kill you
b. So much still unknown
c. So many places where drug can work wrong
d. Target identification and validation: find enzyme
e. Lead identification and optimization: find molecule to inhibit it
f. Preclinical development: check safety and effectiveness before humans
g. Human clinical trials phases 1-3
h. Reg approval
III. Ch. 4: drug R&D and the organizational challenges
a. Not like processor design; very little knowledge about entire system and overall spec
b. Process very complex and can’t be broken into pieces: uncertainty and integrality
c. Most R&D on losers
d. Active ingredient and formulation both matter
e. New scientific advances increase uncertainty; show more what we don’t know
f. More choice means more uncertainty
g. More advances mean harder integreation
Part 2: The business of the science
I. Ch. 5: the anatomy of a science-based business
a. Many separate technologies
b. Cyclical entry
c. Genentech started industry
i. Close links to universities
ii. Biz model innovation: contract w/ big pharma for funding development of drug and royalties in exchange for manufacturing and marketing rights
1. First time pharma did R&D through external for-profit co
iii. Pursuit of broad range of opportunities/diseases
d. Second generation used more chemistry and focused on research, allowing pharma to commercialize
e. Third gen: human genomics, industrialized R&D, platform strategy
f. Market for know-how
i. More collab w/ biotechs than w/ univ
II. Ch. 6: the performance of the biotech industry: promise vs. reality
a. Long lag times
b. Zero industry profits
c. Huge skews for Amgen and Genentech
d. R&D productivity, revenue-adjusted
III. Ch. 7: monetizing IP
a. Txr of IP from univ -> private new firms
b. Capital markets (VC) and public equity
c. Market for know-how (small firms trade IP for funding from big firms)
d. Go public much earlier for funding
i. Only 20% of public co’s today have ANY product on market, so basically R&D entities ) (GAAP not as useful)
e. Univ research -> startup w/ VC -> IPO for more funding -> license to big co to bring to patient
f. 3 requirements for risk mgmt.
i. Many options for diversification
ii. Adequate info
iii. Abilty to reap reward
g. Market for know-how -> integration
i. But biotech less modular and codified than software
ii. IP protection murky
IV. Ch. 8: organizational strategies and business models
a. Few examples of success, high uncertainty, luck plays big role
b. Financing critical for industry and its main measure, but wrong measure because it’s input, not performance
c. Alliances/IP monetization are important but not endgame
d. Movie studio model for big pharma: produce ideas of independent writers
V. Ch. 9: The path ahead
a. Venture philanthropy
b. Rethinking the publicly held biotech firm (doesn’t match 10 year investment cycle)